Attribute Reduction Method Research Articles

Novel fuzzy [formula omitted]-covering rough set models and their applications

In recent years, fuzzy β-covering, as a natural extension of fuzzy coverings, has attracted considerable attention. However, existing fuzzy β-neighborhood operators cannot accurately describe the relationship between objects, which greatly restricts the application of fuzzy β-covering. For this reason, we first construct four new fuzzy β-neighborhood operators by using the existing fuzzy β-neighborhood operator and generalized fuzzy logic operators, and investigate their properties. To better portray the similarity between samples, inspired by the definition of fuzzy similarity relation, we define the concept of fuzzy β-covering relation. On this basis, we develop a new framework of fuzzy β-covering rough set models. We further propose an attribute reduction method by employing the new fuzzy β-covering relation, and design a heuristic attribute reduction algorithm with reference to an uncertainty measure called attribute significance. Finally, experimental results show the superiority of our proposed method through a series of experimental analyses.

Attribute reduction based on observational consistency in intuitionistic fuzzy multi-covering decision systems

Attribute reduction is an important issue in data mining, machine learning and other applications of big data processing. Covering-based rough set and intuitionistic fuzzy (IF) set models are both the effective theoretical tools of uncertainty or imprecise computation, and thus IF covering rough set model has been acknowledged as a positive approach to attribute reduction. Based on IF covering rough set model, this study explores a kind of parameterized IF observational consistency in IF multi-covering decision system, and proposes an attribute reduction method. This article firstly defines the concepts of regular IF β-covering, parameterized IF observational sets on the regular IF β-covering approximation space. Secondly, the parameterized IF observational consistency is defined to be the principal of attribute reduction in the IF multi-covering decision system, and the related IF discernibility matrix is developed to provide a way of attribute reduction. For multi-observational consistency corresponding to an observational parameters set, an unified multi-observational discernibility matrix is constructed, which avoids the disadvantage of needing to construct multiple corresponding discernibility matrices separately. Furthermore, an attribute reduction algorithm based on iterative dissolving of unified multi-observational discernibility matrix is proposed, and the experiment to demonstrate effectiveness of algorithm is presented. Experiments with UCI datasets shows that, the proposed method is a good way for improving both the rates of attribute-reduced and the classification accuracy of reduced datasets.

A novel attribute reduction method based on intuitionistic fuzzy three-way cognitive clustering

A novel attribute reduction method based on intuitionistic fuzzy three-way cognitive clustering

Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application

In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction.

Exploring interactive attribute reduction via fuzzy complementary entropy for unlabeled mixed data

Attribute reduction is one of the important applications in fuzzy rough set theory. However, most attribute reduction methods in fuzzy rough theory mainly focus on removing irrelevant or redundant attributes. There are few reports about the method of considering attribute interaction. For this reason, this paper proposes an interactive attribute reduction method for unlabeled mixed data. First, some uncertainty measures based on fuzzy complementary entropy are further defined. Then, based on the proposed uncertainty measure, the attribute evaluation criteria of maximal information, minimal redundancy, and maximal interactivity are developed respectively. As a result, the evaluation index of the attribute importance is established by using the idea of unsupervised maximal information-minimal redundancy-maximal interactivity. Finally, a corresponding algorithm is designed to select attributes. The experimental results show that the proposed algorithm has better performance.

A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets

Attribute reduction is an important application of rough set theory. Most existing rough set models do not consider the weight information of attributes in information systems. In this paper, we first study the weight of each attribute in information systems by using data binning method and information entropy theory. Then, we propose a new rough set model of weighted neighborhood probabilistic rough sets (WNPRSs) and investigate its basic properties. Meanwhile, the dependency degree formula of an attribute relative to an attribute subset is defined based on WNPRSs. Subsequently, we design a novel attribute reduction method by using WNPRSs and the corresponding algorithm is also given. Finally, to evaluate the performance of the proposed algorithm, we conduct a data experiment and compare it with other existing attribute reduction algorithms on eight public datasets. Experimental result demonstrates that the proposed attribute reduction algorithm is effective and performs better than some of the existing algorithms.

Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model

Attribute reduction is the core research content in rough set theory. At present, the attribute reduction of numerical information system mostly adopts the neighborhood rough set method. In order to further improve the similarity measurement effect between data objects, kernel function method is used to construct a new rough set model in numerical information system, and an uncertainty measurement method and attribute reduction method are proposed. Firstly, the similarity between objects of numerical information system is calculated by kernel function, and a granular structure model and rough set model based on kernel similarity relation are proposed. Then, from the perspective of kernel similarity rough approximation, an information system uncertainty measurement method called kernel approximation precision and kernel approximation roughness is proposed. Because these two measurement methods do not meet the strict monotonicity of information granulation, the concept of kernel knowledge granularity based on kernel similarity granular structure is further proposed in this paper. By combining kernel approximation precision and kernel approximation roughness with kernel knowledge granularity, an uncertainty measurement method of kernel similarity combination measurement is proposed. Finally, using the strict monotonicity of kernel similarity combination measurement, an attribute reduction algorithm for numerical information system is designed. Experimental analysis shows the effectiveness and superiority of the proposed method.

Three-way approximate reduct based on information-theoretic measure

Three-way decision is a typical and popular methodology for decision-making and approximate reasoning, while attribute reduction is an important research topic in three-way decision. However, most attribute reduction methods based on three-way decision strictly rely on the preservation of measure criterion, which not only explicitly limits the efficiency of attribute reduction and also implicitly confines the generalization ability of the resulting reduct. In this study, we present a new three-way approximate attribute reduction method based on information-theoretic measure. More specifically, a unified framework for approximate attribute reduction is first provided. Then, the process of attribute reduction is considered to determine each attribute to be the positive region, boundary region, or negative region in terms of its correlation to the decision attribute. The negative attributes can be removed by the preservation of information-theoretic measure, while some boundary attributes are further iteratively eliminated by relaxing the measure criterion. An approximate reduct is finally formed by the positive attributes and the remaining boundary attributes. On several public UCI data sets, the proposed method achieves a much better attribute reduction rate and simultaneously gains an improvement in performance when comparing with other attribute reduction methods.

An improved D-S evidence theory based neighborhood rough classification approach

Neighborhood classifier, a common classification method, is applied in pattern recognition and data mining. The neighborhood classifier mainly relies on the majority voting strategy to judge each category. This strategy only considers the number of samples in the neighborhood but ignores the distribution of samples, which leads to a decreased classification accuracy. To overcome the shortcomings and improve the classification performance, D-S evidence theory is applied to represent the evidence information support of other samples in the neighborhood, and the distance between samples in the neighborhood is taken into account. In this paper, a novel attribute reduction method of neighborhood rough set with a dynamic updating strategy is developed. Different from the traditional heuristic algorithm, the termination threshold of the proposed reduction algorithm is dynamically optimized. Therefore, when the attribute significance is not monotonic, this method can retrieve a better value, in contrast to the traditional method. Moreover, a new classification approach based on D-S evidence theory is proposed. Compared with the classical neighborhood classifier, this method considers the distribution of samples in the neighborhood, and evidence theory is applied to describe the closeness between samples. Finally, datasets from the UCI database are used to indicate that the improved reduction can achieve a lower neighborhood decision error rate than classical heuristic reduction. In addition, the improved classifier acquires higher classification performance in contrast to the traditional neighborhood classifier. This research provides a new direction for improving the accuracy of neighborhood classification.

A Novel Back Propagation Neural Network Optimized by Rough Set and Particle Swarm Algorithm for Remanufacturing Service Provider Classification and Selection

Aiming at the problem that the high classification feature dimensionality of the back propagation neural network (BPNN) leads to slow convergence speed and the initial weight and threshold sensitivity of the BPNN lead to the problem of easy convergence to the local optimum. A novel BPNN optimized by rough set and particle swarm algorithm (RS-PSO-BPNN) for remanufacturing service provider classification and selection is proposed. First, the attribute reduction method of rough set theory is used to preprocess the classification features of remanufacturing service providers, redundant attributes are deleted from the decision table, and the input feature dimension is reduced; then the PSO algorithm is used to optimize the network Initial weight and threshold. Finally, the proposed method is used for the selection and optimization of remanufacturing service providers. The results show that the proposed RS-PSO-BPNN has higher classification accuracy and efficiency for the problem, which provides scientific decision supports for remanufacturing service provider selection.

Attributes reduction algorithms for m-polar fuzzy relation decision systems

Nowadays, attribute reduction has become a significant topic in relation decision systems. Their applications come from different domains of the computer sciences, including machine learning, data mining and pattern recognition, which often involve a large number of attributes in data. Several attribute reduction methods are presented in the literature in order to help solving decision-making problems efficiently. A common characterization for these approaches is still missing, that is, although attribute reduction methods of relation decision systems and fuzzy relation decision systems exist, a common generalization for them is still missing. This study presents a systematic discussion of attribute reduction based on m-polar fuzzy (mF, in short) relation systems and mF relation decision systems, which are respective extensions of fuzzy relation systems and fuzzy relation decision systems. This study provides mathematical results on the attribute reduction algorithms based upon mF relation systems and mF relation decision systems. Both are explained with numerical examples. The resulting algorithms permit to reinterpret the upshots of traditional reduction methods, providing them with larger generality and unification abilities. Afterwards, two real-life applications of the proposed attribute reduction approaches prove their validity and feasibility. Finally, the attribute reduction methods developed here are compared with some existing approaches to show their reliability.

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction.

The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.

Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels

Attribute reduction is attracting considerable attention in the theory of rough sets, and thus many rough-set-based attribute reduction methods have been presented. However, most of them are specifically designed for either labeled or unlabeled data, whereas many real-world applications involve partial supervision. In this paper, we propose a rough-set-based semi-supervised attribute reduction method for partially labeled data. Specifically, using prior class-distribution information, we first develop a simple yet effective strategy to produce proxy labels for unlabeled data. Then, the concept of information granularity is integrated into an information-theoretic measure, based on which, a novel granular conditional entropy measure is proposed, and its monotonicity is theoretically proved. Furthermore, a fast heuristic algorithm is provided to generate the optimal reduct of partially labeled data, which could accelerate the process of attribute reduction by removing irrelevant examples and simultaneously excluding redundant attributes. Extensive experiments conducted on UCI data sets demonstrate that the proposed semi-supervised attribute reduction method is promising and, in terms of classification performance, it even compares favorably with supervised methods on labeled and unlabeled data with true labels (Our code and experimental data are released at Mendeley Data https://doi.org/10.17632/v3byhx2v8s.1).

A fuzzy [formula omitted]-similarity relation-based attribute reduction approach in incomplete interval-valued information systems

As generalizations of single-valued information systems, interval-valued information systems (IVISs) can better express the real data with uncertainty in some applications. Attribute reduction methods for complete IVISs or complete interval-valued decision systems (IVDSs) have been developed. However, there are few researches on attribute reduction for incomplete interval-valued information systems (IIVISs). The paper aims to investigate the attribute reduction issue in IIVISs. Firstly, the maximal and minimal distances, which characterize the difference between two interval values, are defined, and the maximal and minimal similarity degrees are given. Secondly, the fuzzy α-similarity relation is defined based on similarity between interval values, and the concept of α-equivalence relation is raised. Thirdly, entropy measures are investigated for IIVISs in view of α-equivalence relations. Fourthly, a new attribute reduction approach for IIVISs is proposed by using conditional entropy, and its corresponding algorithm is given. Finally, experiments to verify the effectiveness and feasibility of the newly proposed approach for attribute reduction in IIVISs are presented. These results will be helpful to perfect the uncertainty measurement model, and provide an approach for attribute reduction in IIVISs.

Multigranulation Supertrust Model for Attribute Reduction

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this article, we present a novel multigranulation supertrust fuzzy–rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation supertrust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme was adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular coevolution is employed to ensure a wide range of balancing of exploration and exploitation, and this strategy can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

Research on management and control strategy of E-bikes based on attribute reduction method

Abstract The sports characteristics of E-bikes increase the conflict between traffic participants. In this paper, the criteria of E-bike traffic conflict are given according to time distance. Based on the rough set theory, the redundant rule reduction method is developed, and the following conclusions are drawn: 1. Signal lights and video monitoring points have little effect on alleviating the traffic conflict between e-vehicles and motor vehicles; 2. Setting isolation facilities, non-motor vehicle lanes and traffic police can effectively reduce the traffic conflict. The results show that this method can effectively analyse the main influencing factors of traffic conflict.

Double-local rough sets for efficient data mining

As an important extension of classical rough sets, local rough set model is effective to handle large data sets with small amounts of labeled data, which has an obvious advantage in improving computational performance. However, the existing mining algorithms based on local rough sets are still computationally time-consuming in processing large-scale labeled data sets. To overcome this limitation, we propose an enhanced local rough set framework called double-local rough sets, by introducing the notion of local equivalence classes. Under this framework, we define the lower deletion matrix, the upper addition matrix, and the upper deletion matrix. Based on these matrices, we develop a fast iteration method for computing the approximations, which is vital for attribute reduction and knowledge discovery. Furthermore, a fast attribute reduction method is presented by accelerating the calculation of stop criteria and attribute significance measures, which can obtain the same attribute reduct as its original local rough set version. Theoretical analysis and experimental results indicate that the proposed algorithms in double-local rough sets significantly outperform their original counterparts in classical local rough sets.

Unsupervised attribute reduction for mixed data based on fuzzy rough sets

Unsupervised attribute reduction becomes very challenging due to a lack of decision information, which is to select a subset of attributes that can maintain learning ability without decision information. However, most of the existing unsupervised attribute reduction methods are proposed for numerical or nominal attributes, and little research has been done on unsupervised mixed attribute reduction methods. In view of this, this paper proposes a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets . First, based on all single attribute subsets , the significance is defined to indicate the importance of a candidate attribute. Then, a specific fuzzy rough-based unsupervised attribute reduction (FRUAR) algorithm is designed. Finally, the proposed algorithm is compared with the existing algorithms by using thirty public data sets. Experimental results show that the algorithm FRUAR can select fewer attributes to maintain or improve the performance of learning algorithms, and it is suitable for mixed attribute data.

MapReduce based parallel fuzzy-rough attribute reduction using discernibility matrix

Fuzzy-rough set theory is an efficient method for attribute reduction. It can effectively handle the imprecision and uncertainty of the data in the attribute reduction. Despite its efficacy, current approaches to fuzzy-rough attribute reduction are not efficient for the processing of large data sets due to the requirement of higher space complexities. A limited number of accelerators and parallel/distributed approaches have been proposed for fuzzy-rough attribute reduction in large data sets. However, all of these approaches are dependency measure based methods in which fuzzy similarity matrices are used for performing attribute reduction. Alternative discernibility matrix based attribute reduction methods are found to have less space requirements and more amicable to parallelization in building parallel/distributed algorithms. This paper therefore introduces a fuzzy discernibility matrix-based attribute reduction accelerator (DARA) to accelerate the attribute reduction. DARA is used to build a sequential approach and the corresponding parallel/distributed approach for attribute reduction in large data sets. The proposed approaches are compared to the existing state-of-the-art approaches with a systematic experimental analysis to assess computational efficiency. The experimental study, along with theoretical validation, shows that the proposed approaches are effective and perform better than the current approaches.

A Parallel Attribute Reduction Method Based on Classification

Parallel processing as a method to improve computer performance has become a development trend. Based on rough set theory and divide-and-conquer idea of knowledge reduction, this paper proposes a classification method that supports parallel attribute reduction processing, the method makes the relative positive domain which needs to be calculated repeatedly independent, and the independent relative positive domain calculation could be processed in parallel; thus, attribute reduction could be handled in parallel based on this classification method. Finally, the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets.